Srishti Jain
Physics post-grad automating messy data workflows into clean
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š Portfolio Project: E-Commerce Data Pipeline & Analytics (PostgreSQL)
Processed and analyzed ~500,000 raw transactional logs from the Online Retail II dataset to clean messy operational records and extract executive business intelligence metrics.
š” Key Highlights:
Data Staging & Cleaning: Engineered custom transaction flags (SALE vs RETURN), deduplicated raw records, and stripped non-inventory fee entries (AMAZON FEE, POSTAGE).
Executive Metrics: Isolated $426.07K in Net Revenue across 5,266 distinct orders, evaluating an overall 13.1% return rate.
Customer Lifetime Value (CLV): Segmented Registered vs. Guest checkouts, uncovering top-tier accounts with $27,000+ in lifetime spend.
Time-Series Growth: Implemented advanced SQL window functions (LAG()) to compute Month-over-Month (MoM) revenue growth velocity.
š ļø Tools Used: PostgreSQL | Data Modeling | SQL Analytics | CTEs | Window Functions
š Check out the complete SQL scripts & documentation on GitHub: https://github.com/srishtijain1674/ecommerce-sql-analytics
#DataAnalytics #PostgreSQL #SQL #DataOperations #FreelanceAnalyst
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Certified SQL Database Querying & Data Integrity Guardrails
Officially certified in SQL (Credential ID: SC-339386C326), with a core focus on schema integrity, query optimization, and data operations.
Key Contributions:
Database Querying: Wrote optimized SELECT, WHERE, GROUP BY, and multi-table JOIN queries for data aggregation.
Defensive SQL Logic: Implemented automated safety checks (TRY_CAST, COALESCE, IS NOT NULL) to handle missing data safely.
Data Safety: Utilized SQL Views and staging tables to audit data while keeping raw source datasets untouched.
Tools Used: SQL, Relational Databases, Staging Pipelines, Data Safety Protocols
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GEMA Data Analysis & Pattern Recognition Pipeline
Processed, cleaned, and analyzed complex structured datasets within the GEMA project framework to uncover key underlying patterns.
Key Contributions:
Exploratory Data Analysis (EDA): Executed data profiling to inspect feature distributions, missing values, and record anomalies.
Data Transformation: Standardized variables and applied data cleaning logic to prepare raw inputs for analytical evaluation.
Insight Extraction: Generated metric summaries and visualizations to interpret dataset behaviors effectively.
Tools Used: Data Analysis, Data Cleaning, Pattern Evaluation, Excel/Python
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Retail Store Transaction Analysis & Dashboard ( Excel Project)
Cleaned and standardized retail transaction data, formatting Date/Time fields, handling missing values, and creating a consistent data structure for analysis
ā¢ Built interactive Pivot Table reports to analyse monthly sales performance, product volumes, and payment method distributions for management reporting
ā¢ Designed an interactive executive dashboard using KPI metrics, Slicers, and Timeline controls to monitor sales performance and support data-driven decision-making
ā¢ Identified business trends from transactional data and presented key performance information through structured reports and visualizations
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